CLI library to automate the onboarding process to IBM Watson OpenScale
Project description
IBM Watson Openscale “fastpath” configuration tool. This tool allows the user to get started quickly with Watson OpenScale. * If needed, provision a Lite plan instance for IBM Watson OpenScale * If needed, provision a Lite plan instance for IBM Watson Machine Learning * Drop and re-create the IBM Watson OpenScale datamart instance and datamart database schema * Optionally, deploy a sample machine learning model to the WML instance * Configure the sample model instance to OpenScale, including payload logging, fairness checking, feedback, quality checking, and explainability * Optionally, store up to 7 days of historical payload, fairness, and quality data for the sample model
Before you begin
☁️ You need an IBM Cloud account.
🔑 Create an IBM Cloud API key
⚠️ If you already have a Watson Machine Learning (WML) instance, ensure it’s RC-enabled, learn more about this in the migration instructions.
Installation
To install, use pip or easy_install:
pip install -U ibm-ai-openscale-cli
or
easy_install -U ibm-ai-openscale-cli
Usage
ibm-ai-openscale-cli --help
usage: [-h] -a APIKEY [--env {ypprod,ypqa,ys1dev,icp}] [--resource-group RESOURCE_GROUP] [--postgres POSTGRES] [--postgres-json POSTGRES_JSON] [--icd ICD] [--icd-json ICD_JSON] [--db2 DB2] [--db2-json DB2_JSON] [--wml WML] [--wml-json WML_JSON] [--azure AZURE] [--azure-json AZURE_JSON] [--spss SPSS] [--spss-json SPSS_JSON] [--custom CUSTOM] [--custom-json CUSTOM_JSON] [--aws AWS] [--aws-json AWS_JSON] [--deployment-name DEPLOYMENT_NAME] [--username USERNAME] [--password PASSWORD] [--url URL] [--datamart-name DATAMART_NAME] [--keep-schema] [--history HISTORY] [--verbose] [--version] [--model {all,GermanCreditRiskModel,DrugSelectionModel,GolfModel}] [--reset {metrics,monitors,datamart,model}] IBM Watson Openscale "fastpath" configuration tool. This tool allows the user to get started quickly with Watson OpenScale: 1) If needed, provision a Lite plan instance for IBM Watson OpenScale 2) If needed, provision a Lite plan instance for IBM Watson Machine Learning 3) Drop and re-create the IBM Watson OpenScale datamart instance and datamart database schema 4) Optionally, deploy a sample machine learning model to the WML instance 5) Configure the sample model instance to OpenScale, including payload logging, fairness checking, feedback, quality checking, and explainability 6) Optionally, store up to 7 days of historical payload, fairness, and quality data for the sample model optional arguments: -h, --help show this help message and exit --env {ypprod,ypqa,ys1dev,icp} Environment. Default "ypprod" --resource-group RESOURCE_GROUP Resource Group to use. If not specified, then "default" group is used --postgres POSTGRES Path to postgres credentials file for the datamart database. If --postgres, --icd, and --db2 all are not specified, then the internal Watson OpenScale database is used --postgres-json POSTGRES_JSON Postgres credentials in JSON format --icd ICD Path to IBM Cloud Database credentials file for the datamart database --icd-json ICD_JSON IBM Cloud Database credentials for the datamart database in JSON format --db2 DB2 Path to IBM DB2 credentials file for the datamart database --db2-json DB2_JSON IBM DB2 credentials for the datamart database in JSON format: '{ "username": "<USERNAME>", "password": "<PASSWORD>", "hostname": "<hostname>", "port": "<port>", "db": "<db>" }' --wml WML Path to IBM WML credentials file --wml-json WML_JSON IBM WML credentials in JSON format --azure AZURE Path to Microsoft Azure credentials file --azure-json AZURE_JSON Microsoft Azure credentials in JSON format: '{ "client_id": "<CLIENT_ID", "client_secret": "<CLIENT_SECRET", "tenant": "<TENANT>", "subscription_id": "<SUBSCRIPTION_ID" }' --spss SPSS Path to SPSS credentials file --spss-json SPSS_JSON SPSS credentials in JSON format: '{ "username": "<USERNAME>", "password": "<PASSWORD", "url": "<URL>" }' --custom CUSTOM Path to Custom Engine credentials file --custom-json CUSTOM_JSON Custom Engine credentials in JSON format: '{ "url": "<URL>" }' --aws AWS Path to Amazon Web Services credentials file --aws-json AWS_JSON Amazon Web Services credentials in JSON format: '{ "access_key_id": "<ACCESS_KEY_ID", "secret_access_key": "<SECRET_ACCESS_KEY", "region": "<REGION>" }' --deployment-name DEPLOYMENT_NAME Name of the existing deployment to use. Required for Azure ML Studio, SPSS Engine and Custom ML Engine. Optional for Watson Machine Learning --username USERNAME ICP username. Required if "icp" environment is chosen --password PASSWORD ICP password. Required if "icp" environment is chosen --url URL ICP url. Required if "icp" environment is chosen --datamart-name DATAMART_NAME Specify data mart name and database schema, default is "aiosfastpath" --keep-schema Use pre-existing datamart schema, only dropping all tables. If not specified, datamart schema is dropped and re-created --history HISTORY Days of history to preload. Default is 7 --verbose verbose flag --version show program's version number and exit --model {all,GermanCreditRiskModel,DrugSelectionModel,GolfModel} Model to set up with Watson OpenScale (default "GermanCreditRiskModel") --reset {metrics,monitors,datamart,model} Reset existing datamart then exit required arguments: -a APIKEY, --apikey APIKEY IBM Cloud platform user APIKey. If "--env icp" is also specified, APIKey value is not used.
Examples
In this example, if a WML instance already exists it is used, but if not a new Lite plan instance is provisioned and used. If an OpenScale instance exists, its datamart is dropped and recreated along with its datamart internal database schema. Otherwise, a Lite plan OpenScale instance is provisioned. The GermanCreditRiskModel is stored and deployed in WML, configured to OpenScale, and 7 days’ historical data stored.
export APIKEY=<PLATFORM_API_KEY>
ibm-ai-openscale-cli --apikey $APIKEY
In this example, assume the user already has provisioned instances of WML, OpenScale, IBM Cloud Database for Postgres (ICD), and has selected a schema for the OpenScale datamart database. The OpenScale datamart is dropped and recreated, and the datamart’s database schema is dropped and recreated. An already-deployed instance of the DrugSelectionModel is configured to OpenScale, and 7 days’ historical data stored.
export APIKEY=<PLATFORM_API_KEY>
export WML=<WML instance credentials JSON file>
export ICD=<ICD instance credentials JSON file>
export SCHEMA=<ICD database schema>
ibm-ai-openscale-cli --apikey $APIKEY --wml $WML --model DrugSelectionModel --deployment-name DrugSelectionModelDeployment --icd $ICD --datamart-name $SCHEMA
In this example, assume the user already has provisioned an Entry plan instance of IBM DB2 Warehouse on Cloud. The OpenScale datamart’s tables within the user’s existing DB2 schema are dropped and recreated. The GermanCreditRiskModel is stored and deployed in WML, configured to OpenScale, and 7 days’ historical data stored.
export APIKEY=<PLATFORM_API_KEY>
export DB2=<DB2 instance credentials JSON file>
export SCHEMA=<user's DB2 database schema>
ibm-ai-openscale-cli --apikey $APIKEY --db2 $DB2 --datamart-name $SCHEMA --keep-schema
FAQ
Q: How do the reset options work?
A: The reset options each affect a different level of data in the datamart:
--reset metrics : Clean up the payload logging table, monitoring history tables etc, so that it restores the system to a fresh state with datamart configured, model deployments added, all monitors configured, but no actual metrics in the system yet. The system is ready to go. Not supported for Watson OpenScale internal databases.
--reset monitors : Remove all configured monitors and corresponding metrics and history, but leave the actual model deployments (if any) in the datamart. User can proceed to configure the monitors via user interface, API, or fastpath.
-- reset datamart : “Factory reset” the datamart to a fresh state as if there was not any configuration.
-- reset model : Delete the model and deployment from WML. Not yet supported for non-WML engines. Does not affect the datamart.
Q: Can I use SSL for connecting to the datamart DB2 database?
A: Yes. DB2 Warehouse on Cloud databases automatically support SSL, using the VCAP json file generated on the “Service Credentials” page. For on-prem or ICP4D DB2 databases, you can specify the path on the local client machine to a copy of the DB2 server’s SSL certificate “arm” file, using an “ssldsn” connection string in the VCAP json file:
{ "hostname": "<ipaddr>", "host": "<ipaddr>", "username": "<uid>", "password": "<pw>", "port": 50000, "jdbcurl": "jdbc:db2://<ipaddr>:50000/<dbname>", "uri": "db2://<uid>:<pw>@<ipaddr>:50000/<dbname>", "db": "<dbname>", "dsn": "DATABASE=<dbname>;HOSTNAME=<ipaddr>;PORT=50000;PROTOCOL=TCPIP;UID=<uid>;PWD=<pw>", "ssldsn": "DATABASE=<dbname>;HOSTNAME=<ipaddr>;PORT=50001;PROTOCOL=TCPIP;UID=<uid>;PWD=<pw>;Security=ssl;SSLServerCertificate=/path_on_local_client_machine_to/db2server_instance.arm;" }
If SSL connections are not needed, or not configured on the DB2 server, you can remove the “ssldsn” tag and ibm-ai-openscale-cli will use the non-SSL “dsn” tag instead. If the VCAP has both dsn and ssldsn tags, ibm-ai-openscale-cli will use “ssldsn” tag to create an SSL connection.
Python version
✅ Tested on Python 3.4, 3.5, 3.6, and 3.7.
Contributing
See CONTRIBUTING.md.
License
This library is licensed under the Apache 2.0 license.
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